Relational Graph Neural Network with Neighbor Interactions for Bundle Recommendation Service

Bundle recommendation plays a crucial role in the service ecosystem. However, most existing bundle recommendation methods are limited in several critical aspects such as the lack of injecting different relations into the representations of bundles and items, and the ignorance of neighbor interactions. To address these limitations, in this paper, we propose a relational graph neural network with neighbor interactions for bundle recommendation. Specifically, we firstly construct two relational graphs, e.g., user-bundle-item interaction graph and bundle-item affiliation graph. We utilize a relational graph neural network to inject different relations into representations of bundles and items. Secondly, we consider neighbor interactions to highlight common properties of neighbors. Finally, a multi-task learning framework is also exploited to capture users' preferences at the item level to further enhance bundle recommendation performance. Comprehensive experiments on two real-world public datasets demonstrate that our proposed method can outperform various representative bundle recommendation methods.